Latent Dirichlet Allocation Coupled with Time Series Analyses
Calculate AICc
Produce the autocorrelation panel for the TS diagnostic plot of a para...
Check that a set of change point locations is proper
Check that a control list is proper
Check that the document covariate table is proper
Check that document term table is proper
Check that a formula is proper
Check that formulas vector is proper and append the response variable
Check that LDA model input is proper
Check that nchangepoints vector is proper
Check that nseeds value or seeds vector is proper
Check that the time vector is proper
Check that topics vector is proper
Check that weights vector is proper
Count trips of the ptMCMC particles
Calculate ptMCMC summary diagnostics
Calculate document weights for a corpus
Produce the posterior distribution ECDF panel for the TS diagnostic pl...
Use ptMCMC to estimate the distribution of change point locations
Estimate the distribution of regressors, unconditional on the change p...
Expand the TS models across the factorial combination of LDA models, f...
Replace if TRUE
Create the model-running-message for an LDA
Run a set of Latent Dirichlet Allocation models
Create control list for set of LDA models
Run a full set of Latent Dirichlet Allocations and Time Series models
Create the controls list for the LDATS model
Package to conduct two-stage analyses combining Latent Dirichlet Alloc...
Calculate the log likelihood of a VEM LDA model fit
Log likelihood of a multinomial TS model
Determine the log likelihood of a Time Series model
Calculate the log-sum-exponential (LSE) of a vector
Logical control on whether or not to memoise
Optionally generate a message based on a logical input
Create a properly symmetric variance covariance matrix
Determine the mode of a distribution
Fit a multinomial change point Time Series model
Fit a multinomial Time Series model chunk
Normalize a vector
Package the output of the chunk-level multinomial models into a multin...
Package the output from LDA_set
Package the output of LDA_TS
Summarize the Time Series model
Package the output of TS_on_LDA
Plot a set of LDATS LDA models
Plot the key results from a full LDATS analysis
Plot the results of an LDATS LDA model
Plot an LDATS TS model
Produce the posterior distribution histogram panel for the TS diagnost...
Prepare the time chunk table for a multinomial change point Time Serie...
Initialize and update the change point matrix used in the ptMCMC algor...
Initialize and update the chain ids throughout the ptMCMC algorithm
Set the control inputs to include the seed
Initialize and tick through the progress bar
Pre-calculate the change point proposal distribution for the ptMCMC al...
Prepare the inputs for the ptMCMC algorithm estimation of change point...
Prepare and update the data structures to save the ptMCMC output
Prepare the ptMCMC temperature sequence
Prepare the model-specific data to be used in the TS analysis of LDA o...
Print the selected LDA and TS models of LDA_TS object
Print a Time Series model fit
Print a set of Time Series models fit to LDAs
Print the message to the console about which combination of the Time S...
Fit the chunk-level models to a time series, given a set of proposed c...
Add change point location lines to the time series plot
Select the best LDA model(s) for use in time series
Select the best Time Series model
Prepare the colors to be used in the gamma time series
Prepare the colors to be used in the LDA plots
Create the list of colors for the LDATS summary plot
Prepare the colors to be used in the change point histogram
Create the list of colors for the TS summary plot
Simulate LDA data from an LDA structure given parameters
Simulate LDA_TS data from LDA and TS model structures and parameters
Simulate TS data from a TS model structure given parameters
Calculate the softmax of a vector or matrix of values
Conduct a within-chain step of the ptMCMC algorithm
Summarize the regressor (eta) distributions
Summarize the rho distributions
Conduct a set of among-chain swaps for the ptMCMC algorithm
Produce the trace plot panel for the TS diagnostic plot of a parameter
Conduct a single multinomial Bayesian Time Series analysis
Create the controls list for the Time Series model
Plot the diagnostics of the parameters fit in a TS model
Conduct a set of Time Series analyses on a set of LDA models
Create the summary plot for a TS fit to an LDA model
Verify the change points of a multinomial time series model
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
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